What are Generators in Python and How to use them?

Python Generators

What are generators in Python?

There is a lot of overhead in building an iterator in Python. we have to implement a class with __iter__() and __next__() method, keep track of internal states, raise StopIteration when there was no values to be returned etc.

How to create a generator in Python?

It is very easy to create a generator in Python programming. It is as easy as defining a normal function with yield statement instead of a return statement.

The main difference between normal function and Python Generators is that the return statement terminates the function entirely; yield statement pauses the function saving all its states and variable values and later continues from there on successive calls.

See the following example of Generators in Python.

def createGenerator():
    i = 1
    print('Python')
    print(i)
    yield i
 
    i += 1
    print('PHP')
    print(i)
    yield i
 
    i += 1
    print('java')
    print(i)
    yield i
 
gen = createGenerator()
next(gen)
next(gen)
next(gen)

See the following output of above python program:

 Python
 1
 PHP
 2
 java
 3

One interesting thing to note in the above example is that the value of the variable i is remembered between each call.

Unlike any normal functions, the local variables are not destroyed when the function yields. In addition to that, the generator object can be iterated only once.

For Loops with Generators

We can use the python for loop with Generators. And display the values with executing the next() function.

You can see the following example:

def createGenerator():
    i = 1
    print('Ronin')
    yield i
 
    i += 1
    print('RedSkull')
    yield i
 
    i += 1
    print('Rocket')
    yield i
 
for g in createGenerator():
    print(g)

See the following output of the above python program:

 python
 1
 PHP
 2
 Java
 3

Python Generator

Simple generators can be easily created on the fly using generator expressions. It makes constructing generators easy.

The syntax for generator expression is similar to that of the list comprehension in Python. But the square brackets are replaced with round parentheses.

The significant difference between the list comprehension and the generator expression is that while list comprehension produces the entire list, generator expression produces one item at a time.

They are kind of lazy, producing items only when asked for. For this reason, a generator expression is much more memory efficient than list comprehension.

See the below example.

listK = [18, 19, 21, 29, 46]
result = (i ** 2 for i in listK)
print(next(result))
print(next(result))
print(next(result))
print(next(result))

See the following output of the above python program:

 324
 361
 441
 841

Generator expression can be used inside the functions. When used in such a way, the round parentheses can be used.

We can see above that the generator expression did not produce the required result immediately. Instead, it returned a generator object which produces elements on demand.

Python Yield

The yield statement suspends the function’s execution and sends the value back to the caller, but retains enough state to enable function to resume where it is left off.

When resumed, a function continues execution immediately after the last yield run.

This allows its code to produce the series of values over time, rather than computing them at once and sending them back like a list.

Yield is used in Python generators.

The generator function is defined as a regular function, but whenever it needs to generate a value, it does so with a yield keyword rather than return.

If the body of the def contains yield, the function automatically becomes a generator function.

Differences between Generator and a Normal function

Here we will discuss differences between Generator and a Normal function:

1. Generator function contains one or more yield statements.
2. When the generator function called, it returns an object (iterator) but does not start execution immediately.
3. Functions like iter() and next() are implemented automatically. So we can iterate through the elements using the next() function.
4. Once the function yield statement is executed, the function is paused and transfers the control to the caller.
5. Local variables and their states are remembered between successive calls.
6. Finally, when the function terminates, StopIteration is raised automatically on further calls.

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips